A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot by Sachin Govil, Brendan T. Crabb, Yu Deng, Laura Dal Toso, Esther Puyol-Antón, Kuberan Pushparajah, Sanjeet Hegde, James C. Perry, Jeffrey H. Omens, Albert Hsiao, Alistair A. Young, Andrew D. McCulloch

“…The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas. …”
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Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling by Ziyu Li, Xiaoqian Wu, Lin Xu, Ming Liu, Cheng Huang

“…Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. …”
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Expert vs. Novices Dimensions of Tagging Behaviour in an Educational Setting = Bir Akademik Çevrede Uzman ve Deneyimsiz Kullanıcıların Etiketleme Davranışlarının Boyutları by Špiranec, Sonja, Borovac, Mislav

“…This has begun to change with the development of folksonomies as alternative, user-generated models of organizing information. The aim of this paper is to research the efficiency in tagging and folksonomy. …”
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A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data by Kamal Uddin Sarker, Mohammed Saqib, Raza Hasan, Salman Mahmood, Saqib Hussain, Ali Abbas, Aziz Deraman

“…Machine learning applications generate models for in-depth data analysis and decision making. …”
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Synthetic ECG Signal Generation Using Probabilistic Diffusion Models by Edmonmd Adib, Amanda S. Fernandez, Fatemeh Afghah, John J. Prevost

“…Particularly, the Improved Denoising Diffusion Probabilistic Models (DDPM) have shown superiority in image quality to the state-of-the-art generative models, which motivated us to investigate their capability in the generation of the synthetic electrocardiogram (ECG) signals. …”
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Power System Stabilizer as a Part of a Generator MPC Adaptive Predictive Control System by Paweł Sokólski, Tomasz A. Rutkowski, Bartosz Ceran, Dariusz Horla, Daria Złotecka

“…In this paper, a model predictive controller based on a generator model for prediction purposes is proposed to replace a standard generator controller with a stabilizer of a power system. …”
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TrackArt : the user friendly interface for single molecule tracking data analysis and simulation applied to complex diffusion in mica supported lipid bilayers by Matysik, Artur, Kraut, Rachel Susan

“…Finally, TrackArt provides a simulation module, allowing the user to generate models with multiple particle trajectories, diffusing with different characteristics. …”
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Towards resource-aware dialogue systems and sentiment analysis by Pandelea, Vlad

“…Moreover, despite the rise of generative models such as ChatGPT, retrieval-based dialogue systems remain a promising approach due to their ability to deliver syntactically rich and informative responses while allowing for greater control on the responses that the model can provide, which may be critical in some applications. …”
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Target costing implementation decision and organizational capabilities from the perspectives of the balanced scorecard model: a study on Malaysian automotive industry / Hussein Hus... by Sharaf Addin, Hussein Hussein Hamood

“…The main contributions of the study include: applying BSC's perspectives to measure OCs (literature), using company strategy as a moderating variable, using industry type as a control variable, Rasch Model analysis in the pilot study as well as in the main study using its outputs as implications for SEM analysis (methodology), and providing further understanding on integrating TC and BSC as well as developing a conceptual framework for the integration of TC and OCs from BSC generation model based on the findings' (knowledge/findings).…”
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Development of triga mark ii research reactor core monitoring system using adaptive neuro-fuzzy inference system by Mohd Ali, Nur Syazwani

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Encoder-decoder neural networks by Kalchbrenner, N

“…Encoder-decoder neural networks are probabilistic conditional generative models of high-dimensional structured items such as natural language utterances and natural images. …”

Learning methods for robust localization by Chen, C

“…</p> <p>Finally, we leverage deep generative models to propose Sequential Invariant Domain Adaptation (SIDA) to mitigate the domain shift problem of the deep neural network based localization models. …”

Development of triga mark ii research reactor core monitoring system using adaptive neuro-fuzzy inference system by Mohd. Ali, Nur Syazwani

“…Each parameter was generated with 12 -14 fuzzy inference system (FIS) models according to input-partitioning types. The generated model then underwent the training and testing phases to identify the good fit models which can be calculated based on three statistical calculations which are correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) to be further validated using a novel dataset. …”
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Transformers for mapping burned areas in Brazilian Pantanal and Amazon with PlanetScope imagery by Diogo Nunes Gonçalves, José Marcato, Junior, André Caceres Carrilho, Plabiany Rodrigo Acosta, Ana Paula Marques Ramos, Felipe David Georges Gomes, Lucas Prado Osco, Maxwell da Rosa Oliveira, José Augusto Correa Martins, Geraldo Alves Damasceno, Júnior, Márcio Santos de Araújo, Jonathan Li, Fábio Roque, Leonardo de Faria Peres, Wesley Nunes Gonçalves, Renata Libonati

“…When applying the generated model in two Brazilian Amazon forest regions, we achieved F1-score averages of 95.88% for burned areas. …”
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The Relationship between the Low-Carbon Industrial Model and Human Well-Being: A Case Study of the Electric Power Industry by Ying Zhang, Xiaobin Dong, Xuechao Wang, Peng Zhang, Mengxue Liu, Yufang Zhang, Ruiming Xiao

“…The electric power industry is one of the major industries in terms of carbon dioxide (CO₂) emissions, and it is necessary to explore low-carbon green power generation models. In recent years, more research has focused on the difference in carbon emissions in fossil energy versus renewable energy but ignored the impact of energy on human well-being. …”
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Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned dat... by Christopher A. Hempel, Christopher A. Hempel, Dominik Buchner, Leoni Mack, Marie V. Brasseur, Dan Tulpan, Dan Tulpan, Florian Leese, Florian Leese, Dirk Steinke, Dirk Steinke

“…We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods.ResultsSequencing and data-processing methods had a substantial impact on stressor prediction. …”
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Validation of RSM Predicted Optimum Scaling-Up Factors for Generating Electricity in a DCMFC: MATLAB Design and Simulation Model by Khaya Pearlman Shabangu, Nhlanhla Mthembu, Manimagalay Chetty, Babatunde Femi Bakare

“…Moreover, the findings of the current study demonstrate that the DCMFC can be further upscaled through a series connection in a fed-batch mode of operation using a well-designed and simulated process control system that has been computationally designed and modelled using first order MFC model bioenergy generating models MATLAB Simulink and Simscape electrical software. …”
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Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China by Bazel Al-Shaibah, Xingpeng Liu, Jiquan Zhang, Zhijun Tong, Mingxi Zhang, Ahmed El-Zeiny, Cheechouyang Faichia, Muhammad Hussain, Muhammad Tayyab

“…Moreover, in calibrations, the highest value of the coefficient of determination (R²) was ≥0.85 with (RMSE) = 0.038; the lowest value of R² was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R² was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. …”
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Maximum Efficient Power Performance Analysis and Multi-Objective Optimization of Two-Stage Thermoelectric Generators by Lei Tian, Lingen Chen, Yanlin Ge, Shuangshuang Shi

“…Based on the established two-stage thermoelectric generator model, this paper further studies its performance. …”
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Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models: case studies from Germany and South Korea by I. Vorobevskii, J. Park, D. Kim, K. Barfus, R. Kronenberg

“…The second one is an unconditional rain generation model based on a double Poisson process (LetItRain) that does not reproduce the input daily values but rather generates time series with consistent rainfall statistics. …”
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